Abstract: A method for capturing evolving data, comprising observing a data graph, identifying a first entity within the graph, defining a first property of the first entity, and identifying a first data value corresponding to the property and the entity. The method further comprises associating a second entity with the first entity at first data value, and forming a dimension of the first entity with associations to other entities within the graph for different data values of the first property. Identifying the first entity comprises identifying a property of the first entity representing a dimension but the property does not relate to other entities through the dimension. Defining the first property comprises identifying an attribute of the first entity that will not change; establishing the attribute as the first property.

Abstract: Systems and methods are provided for bounded error matching of large scale numeric datasets. For example, a method includes generating a first synopsis for a first numeric dataset maintained in a data repository, receiving a user query to search for numeric datasets in the data repository which are related to a second numeric dataset, generating a second synopsis of the second numeric dataset, performing a hounded error matching process based on an error threshold value by comparing the second synopsis to the first synopsis to determine a match score between the first and second numeric datasets, the match score providing a measure of similarity between the first and second synopses, and responsive to results of the bounded error matching process, returning a query result to the user, which includes an identification of the first numeric dataset and the determined match score between the first and second numeric datasets.

Abstract: A method for capturing evolving data, comprising observing a data graph, identifying a first entity within the graph, defining a first property of the first entity, and identifying a first data value corresponding to the property and the entity. The method further comprises associating a second entity with the first entity at first data value, and forming a dimension of the first entity with associations to other entities within the graph for different data values of the first property. Identifying the first entity comprises identifying a property of the first entity representing a dimension but the property does not relate to other entities through the dimension. Defining the first property comprises identifying an attribute of the first entity that will not change; establishing the attribute as the first property.

Abstract: Methods, systems, and computer program products for identifying marginal-influence maximizing nodes in networks are provided herein. A method includes determining, across a network comprising multiple nodes, one or more target nodes in relation to a query node, wherein said determining comprises calculating a matching score for each of the multiple nodes based on attributes identified in a query associated with the query node; computing a probability associated with each edge in the network that connects two or more of the multiple nodes, wherein the probability comprises the likelihood, given the attributes identified in the query, that a given node is to forward information to a node connected thereto via a given edge; and identifying at least one path from the query node to at least one of the target nodes based on the computed probability associated with each edge in the network.

Abstract: Methods, systems, and computer program products for identifying marginal-influence maximizing nodes in networks are provided herein. A method includes determining, across a network comprising multiple nodes, one or more target nodes in relation to a query node, wherein said determining comprises calculating a matching score for each of the multiple nodes based on attributes identified in a query associated with the query node; computing a probability associated with each edge in the network that connects two or more of the multiple nodes, wherein the probability comprises the likelihood, given the attributes identified in the query, that a given node is to forward information to a node connected thereto via a given edge; and identifying at least one path from the query node to at least one of the target nodes based on the computed probability associated with each edge in the network.